Wireless communications systems that include communication stations that have an antenna array and means for adaptive smart antenna processing are known. Such communications stations are sometimes called smart antenna communications stations. When receiving a signal from a subscriber unit, the signals received by each of the antenna array elements are combined by the adaptive smart antenna processing means to provide an estimate of a signal received from a particular subscriber unit. With the smart antenna processing comprising linear spatial processing, each of the complex-valued (i.e., including in-phase I and quadrature Q components) signals received from the antenna elements is weighted in amplitude and phase by a weighting factor and the weighted signals are then summed to provide the estimate. The adaptive smart antenna processing means can then be described by a set of complex valued weights, one for each antenna elements. These complex valued weights in turn can be described as a single complex valued vector of m elements, where m is the number of antenna elements. This can be extended to include spatio-temporal processing, where the signal at each antenna element, rather than being simply weighted in amplitude and phase, is filtered by some complex valued filter, typically for time equalization. Each filter can be described by a complex-valued transfer function or convolving function. The adaptive smart antenna processing of all elements can then be described by a complex valued m-vector of m complex valued convolving functions.
Several methods are known for determining the weight vectors of received signals. These include methods that determine the directions of arrival of signals from subscriber units, and methods that use the spatial characteristics of subscriber units, for example, the spatial signatures. See for example U.S. Pat. Nos. 5,515,378 and 5,642,353 entitled SPATIAL DIVISION MULTIPLE ACCESS WIRELESS COMMUNICATION SYSTEMS, to Roy et al., for methods that use directions of arrival, and U.S. Pat. No. 5,592,490 entitled SPECTRALLY EFFICIENT HIGH CAPACITY WIRELESS COMMUNICATION SYSTEMS, to Barratt et al., and U.S. Pat. No. 5,828,658 entitled SPECTRALLY EFFICIENT HIGH CAPACITY WIRELESS COMMUNICATION SYSTEMS WITH SPATIO-TEMPORAL PROCESSING, to Ottersten et al., for methods that use spatial signatures. So-called "blind" methods determine the weights from the signals themselves, but without resorting to training signals--that is without determining what weights can best estimate a known symbol sequence. Such methods usually use some known characteristic of the signal transmitted by the subscriber unit to determine the best weights to use by constraining the estimate to have this property, and hence are called property restoral methods. Property restoral methods in turn can be classified into two groups. "Partial" property restoral methods restore one or more typically simple properties of the signal without completely reconstructing the modulated received signal, for example by demodulating and then re-modulating. "Decision directed" (DD) methods construct an accurate copy of the signal by making symbol decisions (e.g., demodulating) the received signal.
One example of the first group, partial restoral methods, is the constant modulus (CM) method, which is applicable to communications systems that use a modulation scheme that has a constant modulus, including, for example phase modulation (PM), frequency modulation (FM), phase shift keying (PSK) and frequency shift keying (FSK). See for example J. R. Treichler; M. L. Larimore: "New Processing Techniques Based on the Constant Modulus Algorithm," IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-33, No. 2, pp. 420-431, April 1985. Other partial property restoral techniques include techniques that restore the spectral properties of the signal, such as the spectral self-coherence. Spectral coherence restoral techniques use known spectral coherence properties of any signals received at the antenna array. For example, in certain situations, the signals may be assumed to be cyclo-stationarity, i.e., to have periodic autocorrelation functions. Other methods include those that restore high order statistics, e.g., moments or cumulants. See for example B. Agee, S. Schell, W. Gardner: "Spectral Self-Coherence Restoral: A New Approach to Blind Adaptive Signal Extraction Using Antenna Arrays," Proceedings of the IEEE, vol. 78, No. 4, April 1990, and U.S. Pat. No. 5,260,968 to Gardner, et al., entitled METHOD AND APPARATUS FOR MULTIPLEXING COMMUNICATIONS SIGNALS THROUGH BLIND ADAPTIVE SPATIAL FILTERING, and U.S. Pat. No. 5,255,210 to Gardner, et al., entitled SELF-COHERENCE RESTORING SIGNAL EXTRACTION APPARATUS AND METHOD.
Decision directed methods use the fact that the modulation scheme of the transmitted subscriber unit signal is known, and determine weights that produce a signal (a "reference signal") that has the required modulation scheme, and if transmitted by a remote user, would produce signals at the antenna elements of the array that are "close" to the signals actually received, the reference signal production including making symbol decisions. See for example U.S. patent applications Ser. No. 08/729,390 entitled METHOD & APPARATUS FOR DECISION DIRECTED DEMODULATION USING ANTENNA ARRAYS & SPATIAL PROCESSING to Barratt, et al. (filed Oct. 11, 1996), and Ser. No. 09/153,110 entitled METHOD FOR REFERENCE SIGNAL GENERATION IN THE PRESENCE OF FREQUENCY OFFSETS IN A COMMUNICATIONS STATION WITH SPATIAL PROCESSING to Petrus, et al. (filed Sep. 15, 1998), for descriptions of systems that use decision directed weight determination.
Some iterative methods, including partial restoral methods, for example, the CM method, are known to converge even for low signal-to-noise ratios (SNRs), low signal-to-interference-plus-noise-ratios (SINRs), and high fading situations as would be encountered in communication systems wherein the subscriber units are highly mobile. Such methods are called "iterative weight determining methods with good convergence properties" herein. Methods with good convergence properties may however take many iterations to converge. The CM method, for example, may so take many iterations to converge, and therefore may not converge fast enough in an actual system. For example, in a high mobility system, it is desired to use the weight vector on a current burst that is derived from the current burst's data. This implies rapid calculation of the weights, which may not be possible with the CM method. The decision directed method, on the other hand, is one example of a class of methods that converges rapidly if the initial condition, for example, the initial signal-to-noise ratio (SNR), and signal-to-interference-plus-noise-ratio (SINR) is high, or the initial weight vector is sufficiently close to the correct value. Methods that so converge rapidly if the initial weight vector is sufficiently close to the correct value are called "rapidly converging iterative weight determining methods" herein. Rapidly converging methods such as the DD method are becoming more widely used in smart-antenna based communication stations. When such a method breaks down, say in low SINR or high fading situations, the method may not converge. This problem becomes more severe in communication systems that have many users in the presence of high co-channel interference, that is, high interference from the signals within the conventional channel from other subscriber units when receiving a signal from a particular subscriber unit, such other subscriber units being from the same or from neighboring cells in the case of a cellular system that includes several receiving communications stations, each communicating with a set of subscriber units located within its cell.
In theory, adaptive smart antenna processing permits more than one communication link to exist in a single "conventional" communication channel so long as the subscriber units that share the same conventional channel can be spatially (or spatio-temporally) resolved. A conventional channel includes a frequency channel in a frequency division multiple access (FDMA) system, a time slot in a time division multiple access (TDMA) system (which usually also includes FDMA, so to be precise, the conventional channel is a time and frequency slot), and a code in a code division multiple access (CDMA) system. The conventional channel is then said to be divided into one or more "spatial" channels, and when more than one spatial channel exists per conventional channel, the multiplexing is called space division multiple access (SDMA). SDMA herein is used to mean inclusion of adaptive smart antenna processing both with one and with more than one spatial channel per conventional channel.
Rapidly converging methods such as decision directed methods also break down in the presence of high co-channel interference in SDMA systems that have more than one spatial channel per conventional channel.
Therefore, there is a need in the art for a adaptive smart antenna processing method that determines adaptive smart antenna processing weights efficiently in a low signal-to-interference plus noise environment or a high fading environment for SDMA systems that have one spatial channel per conventional channel, and for SDMA systems that have a plurality of spatial channels per conventional channel.
Thus there is a need in the art for weight determination methods that perform well under low SINR and high fading situations, and that converge rapidly, i.e., in a small number of iterations.
Thus there is a need in the art for a method that combines good convergence properties with rapid convergence properties.
Thus there is a need in the art for a "blind" method (i.e., one not using training data) that combines good convergence properties (convergence when the SINR is low) with rapid convergence.